Qwen3.5 prefill lags Qwen3 Coder in llama.cpp

This is a good reminder that “same size, same quant, same server” does not mean comparable local inference behavior once architectures diverge.

• –Qwen3.5’s official docs describe a new hybrid stack built from Gated Delta Networks plus sparse MoE, while Qwen3-Coder belongs to the older Qwen3 generation that local runtimes have had longer to tune
• –The Reddit comparison is not apples to apples: Qwen3.5 was run with `--n-cpu-moe 1`, Qwen3-Coder with `--n-cpu-moe 33`, and both were given a very large 200K context window on a 16GB GPU
• –A llama.cpp maintainer recommended switching from manual MoE placement to `--fit on`, and another commenter suggested tuning `-b`, `-ub`, `-fa on`, and `--fit-ctx` to improve prefill speed
• –The thread matters because local model UX is increasingly gated by prompt ingestion and context handling, not just token/sec once generation starts
• –For AI coding agents, slower prefill can still be an acceptable trade if Qwen3.5 delivers more stable long-context behavior and stronger real-world tool use